Document processing devices, such as copiers, scanners, and digital facsimile machines, are increasingly able to handle digitally equivalent versions of paper documents, which can contain digitized text and pictorial, graphical, and other data. However, further processing is often needed to convert raw digital images into an electronically usable form, such as needed for pattern recognition, document classification and retrieval, and other tasks. For example, digital images must often be broken down or “decomposed” into constituent parts or “zones.”
Post-digitization image decomposition can be problematic particularly when a large volume of documents are being converted, thereby rendering manual document decomposition impracticable. Conventional page decomposition generally involves bottom-up, top-down, or hybrid methodologies. Bottom-up approaches, such as the Block Adjoining Graph method, detect individual connected components, which are progressively aggregated into higher level structures, such as words, lines, paragraphs, and so forth. Top-down approaches, such as the X-Y Tree method, recursively split a digital image into rectangular areas by alternating horizontal and vertical cuts along white space. These methodologies are typically implemented through ad hoc rules that can be brittle and which often produce varying results, even with little actual change in the data.
Therefore, there is a need for a non-rule based approach to decomposing digital images into constituent parts or zones without a reliance on specific visual aspects, such as connected components, graphical features, and white space.